Non-interactive learning for navigation.
Learning from passive (non-interactive) data has emerged as a good recipe for learning navigation policies. Behavior cloning learns navigation policies from expert action supervision [1]}, [2]}, [3]}, [4]}, [5]}, but typically underperforms for complex navigation tasks [6]}, [7]} and may require subsequent RL finetuning [5]}, [2]}. Prior work also focuses on pre-training visual representation from image supervision [10]}, [2]}, environment-level representations from videos [12]}, and learning navigation subroutines from videos [13]}. However, they require subsequent policy learning to solve specific navigation tasks of interest. Recent work on self-supervised navigation directly learns distance and semantic scoring functions using passive image [7]} and video [15]}, [6]} collections, and uses analytical strategies for performing ImageGoal and ObjectGoal navigation. In contrast, we propose a supervised strategy to learn object search policies from a passive dataset of 2D semantic maps, and demonstrate state-of-the-art ObjectNav performance with high computational-efficiency for training.
